import logging
import os
from io import BytesIO
from pathlib import Path
from typing import Any

import librosa
import numpy as np
import scipy
import soundfile as sf
from librosa import effects, filters, magphase, pyin

logger = logging.getLogger(__name__)

# For using kwargs
# pylint: disable=unused-argument


def build_mel_basis(
    *,
    sample_rate: int,
    fft_size: int,
    num_mels: int,
    mel_fmin: int,
    mel_fmax: int | None = None,
    **kwargs,
) -> np.ndarray:
    """Build melspectrogram basis.

    Returns:
        np.ndarray: melspectrogram basis.
    """
    if mel_fmax is not None:
        assert mel_fmax <= sample_rate // 2
        assert mel_fmax - mel_fmin > 0
    return filters.mel(sr=sample_rate, n_fft=fft_size, n_mels=num_mels, fmin=mel_fmin, fmax=mel_fmax)


def millisec_to_length(*, frame_length_ms: float, frame_shift_ms: float, sample_rate: int, **kwargs) -> tuple[int, int]:
    """Compute hop and window length from milliseconds.

    Returns:
        Tuple[int, int]: hop length and window length for STFT.
    """
    factor = frame_length_ms / frame_shift_ms
    assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_ms"
    win_length = int(frame_length_ms / 1000.0 * sample_rate)
    hop_length = int(win_length / float(factor))
    return win_length, hop_length


def _log(x, base):
    if base == 10:
        return np.log10(x)
    return np.log(x)


def _exp(x, base):
    if base == 10:
        return np.power(10, x)
    return np.exp(x)


def amp_to_db(*, x: np.ndarray, gain: float = 1, base: float = 10, **kwargs) -> np.ndarray:
    """Convert amplitude values to decibels.

    Args:
        x (np.ndarray): Amplitude spectrogram.
        gain (float): Gain factor. Defaults to 1.
        base (int): Logarithm base. Defaults to 10.

    Returns:
        np.ndarray: Decibels spectrogram.
    """
    assert (x < 0).sum() == 0, " [!] Input values must be non-negative."
    return gain * _log(np.maximum(1e-8, x), base)


# pylint: disable=no-self-use
def db_to_amp(*, x: np.ndarray, gain: float = 1, base: float = 10, **kwargs) -> np.ndarray:
    """Convert decibels spectrogram to amplitude spectrogram.

    Args:
        x (np.ndarray): Decibels spectrogram.
        gain (float): Gain factor. Defaults to 1.
        base (int): Logarithm base. Defaults to 10.

    Returns:
        np.ndarray: Amplitude spectrogram.
    """
    return _exp(x / gain, base)


def preemphasis(*, x: np.ndarray, coef: float = 0.97, **kwargs) -> np.ndarray:
    """Apply pre-emphasis to the audio signal. Useful to reduce the correlation between neighbouring signal values.

    Args:
        x (np.ndarray): Audio signal.

    Raises:
        RuntimeError: Preemphasis coeff is set to 0.

    Returns:
        np.ndarray: Decorrelated audio signal.
    """
    if coef == 0:
        msg = " [!] Preemphasis is set 0.0."
        raise RuntimeError(msg)
    return scipy.signal.lfilter([1, -coef], [1], x)


def deemphasis(*, x: np.ndarray, coef: float = 0.97, **kwargs) -> np.ndarray:
    """Reverse pre-emphasis."""
    if coef == 0:
        msg = " [!] Preemphasis is set 0.0."
        raise ValueError(msg)
    return scipy.signal.lfilter([1], [1, -coef], x)


def spec_to_mel(*, spec: np.ndarray, mel_basis: np.ndarray, **kwargs) -> np.ndarray:
    """Convert a full scale linear spectrogram output of a network to a melspectrogram.

    Args:
        spec (np.ndarray): Normalized full scale linear spectrogram.

    Shapes:
        - spec: :math:`[C, T]`

    Returns:
        np.ndarray: Normalized melspectrogram.
    """
    return np.dot(mel_basis, spec)


def mel_to_spec(*, mel: np.ndarray, mel_basis: np.ndarray, **kwargs) -> np.ndarray:
    """Convert a melspectrogram to full scale spectrogram."""
    assert (mel < 0).sum() == 0, " [!] Input values must be non-negative."
    inv_mel_basis = np.linalg.pinv(mel_basis)
    return np.maximum(1e-10, np.dot(inv_mel_basis, mel))


def wav_to_spec(*, wav: np.ndarray, **kwargs) -> np.ndarray:
    """Compute a spectrogram from a waveform.

    Args:
        wav (np.ndarray): Waveform. Shape :math:`[T_wav,]`

    Returns:
        np.ndarray: Spectrogram. Shape :math:`[C, T_spec]`. :math:`T_spec == T_wav / hop_length`
    """
    D = stft(y=wav, **kwargs)
    S = np.abs(D)
    return S.astype(np.float32)


def wav_to_mel(*, wav: np.ndarray, mel_basis: np.ndarray, **kwargs) -> np.ndarray:
    """Compute a melspectrogram from a waveform."""
    D = stft(y=wav, **kwargs)
    S = spec_to_mel(spec=np.abs(D), mel_basis=mel_basis, **kwargs)
    return S.astype(np.float32)


def spec_to_wav(*, spec: np.ndarray, power: float = 1.5, **kwargs) -> np.ndarray:
    """Convert a spectrogram to a waveform using Griffi-Lim vocoder."""
    S = spec.copy()
    return griffin_lim(spec=S**power, **kwargs)


def mel_to_wav(*, mel: np.ndarray, mel_basis: np.ndarray, power: float = 1.5, **kwargs) -> np.ndarray:
    """Convert a melspectrogram to a waveform using Griffi-Lim vocoder."""
    S = mel.copy()
    S = mel_to_spec(mel=S, mel_basis=mel_basis)  # Convert back to linear
    return griffin_lim(spec=S**power, **kwargs)


### STFT and ISTFT ###
def stft(
    *,
    y: np.ndarray,
    fft_size: int,
    hop_length: int | None = None,
    win_length: int | None = None,
    pad_mode: str = "reflect",
    window: str = "hann",
    center: bool = True,
    **kwargs,
) -> np.ndarray:
    """Librosa STFT wrapper.

    Check http://librosa.org/doc/main/generated/librosa.stft.html argument details.

    Returns:
        np.ndarray: Complex number array.
    """
    return librosa.stft(
        y=y,
        n_fft=fft_size,
        hop_length=hop_length,
        win_length=win_length,
        pad_mode=pad_mode,
        window=window,
        center=center,
    )


def istft(
    *,
    y: np.ndarray,
    hop_length: int | None = None,
    win_length: int | None = None,
    window: str = "hann",
    center: bool = True,
    **kwargs,
) -> np.ndarray:
    """Librosa iSTFT wrapper.

    Check http://librosa.org/doc/main/generated/librosa.istft.html argument details.

    Returns:
        np.ndarray: Complex number array.
    """
    return librosa.istft(y, hop_length=hop_length, win_length=win_length, center=center, window=window)


def griffin_lim(*, spec: np.ndarray, num_iter=60, **kwargs) -> np.ndarray:
    angles = np.exp(2j * np.pi * np.random.rand(*spec.shape))
    S_complex = np.abs(spec).astype(complex)
    y = istft(y=S_complex * angles, **kwargs)
    if not np.isfinite(y).all():
        logger.warning("Waveform is not finite everywhere. Skipping the GL.")
        return np.array([0.0])
    for _ in range(num_iter):
        angles = np.exp(1j * np.angle(stft(y=y, **kwargs)))
        y = istft(y=S_complex * angles, **kwargs)
    return y


def compute_stft_paddings(*, x: np.ndarray, hop_length: int, pad_two_sides: bool = False, **kwargs) -> tuple[int, int]:
    """Compute paddings used by Librosa's STFT.

    Compute right padding (final frame) or both sides padding (first and final frames).
    """
    pad = (x.shape[0] // hop_length + 1) * hop_length - x.shape[0]
    if not pad_two_sides:
        return 0, pad
    return pad // 2, pad // 2 + pad % 2


def compute_f0(
    *,
    x: np.ndarray,
    pitch_fmax: float | None = None,
    pitch_fmin: float | None = None,
    hop_length: int,
    win_length: int,
    sample_rate: int,
    stft_pad_mode: str = "reflect",
    center: bool = True,
    **kwargs,
) -> np.ndarray:
    """Compute pitch (f0) of a waveform using the same parameters used for computing melspectrogram.

    Args:
        x (np.ndarray): Waveform. Shape :math:`[T_wav,]`
        pitch_fmax (float): Pitch max value.
        pitch_fmin (float): Pitch min value.
        hop_length (int): Number of frames between STFT columns.
        win_length (int): STFT window length.
        sample_rate (int): Audio sampling rate.
        stft_pad_mode (str): Padding mode for STFT.
        center (bool): Centered padding.

    Returns:
        np.ndarray: Pitch. Shape :math:`[T_pitch,]`. :math:`T_pitch == T_wav / hop_length`

    Examples:
        >>> WAV_FILE = filename = librosa.example('vibeace')
        >>> from TTS.config import BaseAudioConfig
        >>> from TTS.utils.audio import AudioProcessor
        >>> conf = BaseAudioConfig(pitch_fmax=640, pitch_fmin=1)
        >>> ap = AudioProcessor(**conf)
        >>> wav = ap.load_wav(WAV_FILE, sr=ap.sample_rate)[:5 * ap.sample_rate]
        >>> pitch = ap.compute_f0(wav)
    """
    assert pitch_fmax is not None, " [!] Set `pitch_fmax` before calling `compute_f0`."
    assert pitch_fmin is not None, " [!] Set `pitch_fmin` before calling `compute_f0`."

    if sample_rate / pitch_fmin >= win_length - 1:
        logger.warning("pitch_fmin=%.2f is too small for win_length=%d", pitch_fmin, win_length)
        pitch_fmin = sample_rate / (win_length - 1) + 0.1
        logger.warning("pitch_fmin increased to %f", pitch_fmin)

    f0, voiced_mask, _ = pyin(
        y=x.astype(np.double),
        fmin=pitch_fmin,
        fmax=pitch_fmax,
        sr=sample_rate,
        frame_length=win_length,
        hop_length=hop_length,
        pad_mode=stft_pad_mode,
        center=center,
        n_thresholds=100,
        beta_parameters=(2, 18),
        boltzmann_parameter=2,
        resolution=0.1,
        max_transition_rate=35.92,
        switch_prob=0.01,
        no_trough_prob=0.01,
    )
    f0[~voiced_mask] = 0.0

    return f0


def compute_energy(y: np.ndarray, **kwargs) -> np.ndarray:
    """Compute energy of a waveform using the same parameters used for computing melspectrogram.
    Args:
      x (np.ndarray): Waveform. Shape :math:`[T_wav,]`
    Returns:
      np.ndarray: energy. Shape :math:`[T_energy,]`. :math:`T_energy == T_wav / hop_length`
    Examples:
      >>> WAV_FILE = filename = librosa.example('vibeace')
      >>> from TTS.config import BaseAudioConfig
      >>> from TTS.utils.audio import AudioProcessor
      >>> conf = BaseAudioConfig()
      >>> ap = AudioProcessor(**conf)
      >>> wav = ap.load_wav(WAV_FILE, sr=ap.sample_rate)[:5 * ap.sample_rate]
      >>> energy = ap.compute_energy(wav)
    """
    x = stft(y=y, **kwargs)
    mag, _ = magphase(x)
    return np.sqrt(np.sum(mag**2, axis=0))


### Audio Processing ###
def find_endpoint(
    *,
    wav: np.ndarray,
    trim_db: float = -40,
    sample_rate: int,
    min_silence_sec: float = 0.8,
    gain: float = 1,
    base: float = 10,
    **kwargs,
) -> int:
    """Find the last point without silence at the end of a audio signal.

    Args:
        wav (np.ndarray): Audio signal.
        threshold_db (int, optional): Silence threshold in decibels. Defaults to -40.
        min_silence_sec (float, optional): Ignore silences that are shorter then this in secs. Defaults to 0.8.
        gain (float, optional): Gain factor to be used to convert trim_db to trim_amp. Defaults to 1.
        base (float, optional): Base of the logarithm used to convert trim_db to trim_amp. Defaults to 10.

    Returns:
        int: Last point without silence.
    """
    window_length = int(sample_rate * min_silence_sec)
    hop_length = int(window_length / 4)
    threshold = db_to_amp(x=-trim_db, gain=gain, base=base)
    for x in range(hop_length, len(wav) - window_length, hop_length):
        if np.max(wav[x : x + window_length]) < threshold:
            return x + hop_length
    return len(wav)


def trim_silence(
    *,
    wav: np.ndarray,
    sample_rate: int,
    trim_db: float = 60,
    win_length: int,
    hop_length: int,
    **kwargs,
) -> np.ndarray:
    """Trim silent parts with a threshold and 0.01 sec margin."""
    margin = int(sample_rate * 0.01)
    wav = wav[margin:-margin]
    return effects.trim(wav, top_db=trim_db, frame_length=win_length, hop_length=hop_length)[0]


def volume_norm(*, x: np.ndarray, coef: float = 0.95, **kwargs) -> np.ndarray:
    """Normalize the volume of an audio signal.

    Args:
        x (np.ndarray): Raw waveform.
        coef (float): Coefficient to rescale the maximum value. Defaults to 0.95.

    Returns:
        np.ndarray: Volume normalized waveform.
    """
    return x / abs(x).max() * coef


def rms_norm(*, wav: np.ndarray, db_level: float = -27.0, **kwargs) -> np.ndarray:
    r = 10 ** (db_level / 20)
    a = np.sqrt((len(wav) * (r**2)) / np.sum(wav**2))
    return wav * a


def rms_volume_norm(*, x: np.ndarray, db_level: float = -27.0, **kwargs) -> np.ndarray:
    """Normalize the volume based on RMS of the signal.

    Args:
        x (np.ndarray): Raw waveform.
        db_level (float): Target dB level in RMS. Defaults to -27.0.

    Returns:
        np.ndarray: RMS normalized waveform.
    """
    assert -99 <= db_level <= 0, " [!] db_level should be between -99 and 0"
    return rms_norm(wav=x, db_level=db_level)


def load_wav(
    *, filename: str | os.PathLike[Any], sample_rate: int | None = None, resample: bool = False, **kwargs
) -> np.ndarray:
    """Read a wav file using Librosa and optionally resample, silence trim, volume normalize.

    Resampling slows down loading the file significantly. Therefore it is recommended to resample the file before.

    Args:
        filename (str): Path to the wav file.
        sr (int, optional): Sampling rate for resampling. Defaults to None.
        resample (bool, optional): Resample the audio file when loading. Slows down the I/O time. Defaults to False.

    Returns:
        np.ndarray: Loaded waveform.
    """
    if resample:
        # loading with resampling. It is significantly slower.
        x, _ = librosa.load(filename, sr=sample_rate)
    else:
        # SF is faster than librosa for loading files
        x, _ = sf.read(filename)
    if x.ndim != 1:
        logger.warning("Found multi-channel audio. Converting to mono: %s", filename)
        x = librosa.to_mono(x)
    return x


def save_wav(
    *,
    wav: np.ndarray,
    path: str | os.PathLike[Any] | BytesIO,
    sample_rate: int,
    pipe_out=None,
    do_rms_norm: bool = False,
    db_level: float = -27.0,
    **kwargs,
) -> None:
    """Save float waveform to a file using Scipy.

    Args:
        wav (np.ndarray): Waveform with float values in range [-1, 1] to save.
        path (str): Path to a output file.
        sr (int): Sampling rate used for saving to the file. Defaults to None.
        pipe_out (BytesIO, optional): Flag to stdout the generated TTS wav file for shell pipe.
        do_rms_norm (bool): Whether to apply RMS normalization
        db_level (float): Target dB level in RMS.
    """
    if not isinstance(path, BytesIO):
        path = Path(path)
        path.parent.mkdir(exist_ok=True, parents=True)
        if path.is_dir():
            msg = f"Output path must be a file, not a directory: {path}"
            raise IsADirectoryError(msg)
    if do_rms_norm:
        if db_level is None:
            msg = "`db_level` cannot be None with `do_rms_norm=True`"
            raise ValueError(msg)
        wav_norm = rms_volume_norm(x=wav, db_level=db_level)
    else:
        wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))

    wav_norm = wav_norm.astype(np.int16)
    if pipe_out:
        wav_buffer = BytesIO()
        scipy.io.wavfile.write(wav_buffer, sample_rate, wav_norm)
        wav_buffer.seek(0)
        pipe_out.buffer.write(wav_buffer.read())
    scipy.io.wavfile.write(path, sample_rate, wav_norm)


def mulaw_encode(*, wav: np.ndarray, mulaw_qc: int, **kwargs) -> np.ndarray:
    mu = 2**mulaw_qc - 1
    signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1.0 + mu)
    signal = (signal + 1) / 2 * mu + 0.5
    return np.floor(
        signal,
    )


def mulaw_decode(*, wav, mulaw_qc: int, **kwargs) -> np.ndarray:
    """Recovers waveform from quantized values."""
    mu = 2**mulaw_qc - 1
    return np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)


def encode_16bits(*, x: np.ndarray, **kwargs) -> np.ndarray:
    return np.clip(x * 2**15, -(2**15), 2**15 - 1).astype(np.int16)


def quantize(*, x: np.ndarray, quantize_bits: int, **kwargs) -> np.ndarray:
    """Quantize a waveform to a given number of bits.

    Args:
        x (np.ndarray): Waveform to quantize. Must be normalized into the range `[-1, 1]`.
        quantize_bits (int): Number of quantization bits.

    Returns:
        np.ndarray: Quantized waveform.
    """
    return (x + 1.0) * (2**quantize_bits - 1) / 2


def dequantize(*, x, quantize_bits, **kwargs) -> np.ndarray:
    """Dequantize a waveform from the given number of bits."""
    return 2 * x / (2**quantize_bits - 1) - 1
